The Association Between Mode of Delivery and Later Educational Outcomes
Bibliographic record
Abstract
IntroductionA 2002 report described the gap in health status between First Nations (FN) and all other Manitobans (AOM). That report was widely quoted in the context of other initiatives recognizing the inequities in Canadian society. Objectives and ApproachWe analyzed linked administrative data held in the Manitoba Population Research Data Repository to determine the health status and health care use of First Nations people. To provide context to the findings we compared First Nations to all other Manitobans, disaggregated by on-reserve off-reserve status, and presented our findings by Region and Tribal Council area. The 35 indicators were chosen to address First Nations priorities and providecomparisons with the previous study. Results were age and sex adjusted. ResultsThe gap between FN and AOM has grown. Premature mortality rates are 3x higher for FN compared to AOM. Rates of death by suicides and suicide attempts are 5x higher for FN compared to AOM. Rates of opioid prescribing are 2.5x higher for single prescription, and 4.5x higher for multiple prescriptions for FN compared to AOM. Colorectal cancer screening rates are 2x higher among all other Manitobans compared to FN. Continuity of care is much lower in FN than in AOM. For FN, primary care is less likely to be provided close to home than for AOM Conclusion / ImplicationsDespite initiatives like the Truth and Reconciliation Commission, and Indian Residential School survivors pursuing healing, the gap in health outcomes has increased. Underlying causes such as ongoing systemic racism and colonialism within health governance should be addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".